[Preliminary Research Report] AI Networks for AI Workloads

What are the network design options and topologies to support AI workloads?

The characteristic of emerging AI applications like ChatGPT is the vast number of parameters that must be managed. Some handle billions or even trillions of parameters, requiring thousands to tens of thousands of GPUs, TPUs, and other types of high-speed processors. To connect these accelerated servers into large-scale clusters, a data center-scale fabric known as an AI backend network is necessary. This network differs from traditional frontend networks used for connecting general-purpose servers. Furthermore, AI workloads possess unique attributes and characteristics that are significantly different from traditional general-purpose computing workloads. These distinctive attributes have a crucial impact on the type of network required for executing the workloads. For more details, please visit the URL below. [Report Details] *Reservations now being accepted* https://www.dri.co.jp/auto/report/delloro/230824-de-ad-ai-networks.html

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